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SummaryEnvironmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.

UNAFLOW(UNsteady Aerodynamics for FloatingWind) is a joint EU-IRPWIND founded experiment on wind turbine rotor unsteady aerodynamics. It brings together four different academic contributors: Energy research Centre of the Netherlands (ECN), DTU Wind Energy, University of Stuttgart (USTUTT) and Politecnico di Milano (PoliMi) sharing knowledge both in numerical modelling and in experimental tests design, allowing direct numerical and experimental comparison. The experimental tests carried out for UNAFLOW are of the same type of the ones carried out during the ongoing EU H2020 project LIFES50+ [1], regarding both the unsteady behaviour of the 2d blade section and the entire turbine rotor, although with improved setup and wider test matrix. The project partners are already currently jointly collaborating in the AVATAR project [2], developing and validating numerical models of different accuracy level. The numerical models used in the UNALFOW project range from engineering tool (eg. BEM) to high fidelity CFD methods. Numerical simulations are used both in the design of experiment phase and in the results analysis allowing for an in depth understanding of the experimental findings through advanced modelling approach. The UNAFLOW project, together with a new understanding of the unsteady behaviour of the turbine rotor aerodynamics, will provide also an open database to be shared among the scientific community for future analysis and new models validation.

Population growth, increasing energy demand and the depletion of fossil fuel reserves necessitate a search for sustainable alternatives for electricity generation. Hydropower could replace a large part of the contribution of gas and oil to the present energy mix. However, previous high-resolution estimates of hydropower potential have been local, and have yet to be applied on a global scale. This study is the first to formally present a detailed evaluation of the hydropower potential of each location, based on slope and discharge of each river in the world. The gross theoretical hydropower potential is approximately 52 PWh/year divided over 11.8 million locations. This 52 PWh/year is equal to 33% of the annually required energy, while the present energy production by hydropower plants is just 3% of the annually required energy. The results of this study: all potentially interesting locations for hydroelectric power plants, are available online.

Transport is an important source of air pollution and greenhouse gas emissions. Although the applications of information and communication technologies (ICTs) for transport, also known as intelligent transport systems, are seen as having great potential to help reduce emissions from road transport, their exact impact on CO2 emissions are uncertain for decision makers from government to industry. This uncertainty hinders the deployment of such applications. Therefore there is a need for a common evaluation approach to assess the CO2 impact of ICT measures in a systemic and realistic way. In this study, a methodology framework to evaluate the impact of ICT measures on CO2 emissions is explained. The methodology was developed within the European Union FP7 project Amitran. In particular, this study focuses on the outline and the framework architecture of the methodology as well as the required interfaces between the required models. The use of the methodology is demonstrated by applying it to a use case of dynamic traffic light systems. Finally, the efforts made to validate the methodology and make it accessible to users are explained.

Electric water heaters have the ability to store energy in their water buffer without impacting the comfort of the end user. This feature makes them a prime candidate for residential demand response. However, the stochastic and nonlinear dynamics of electric water heaters, makes it challenging to harness their flexibility. Driven by this challenge, this paper formulates the underlying sequential decision-making problem as a Markov decision process and uses techniques from reinforcement learning. Specifically, we apply an auto-encoder network to find a compact feature representation of the sensor measurements, which helps to mitigate the curse of dimensionality. A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation. In a simulation-based experiment using an electric water heater with 50 temperature sensors, the proposed method was able to achieve good policies much faster than when using the full state information. In a lab experiment, we apply fitted Q-iteration to an electric water heater with eight temperature sensors. Further reducing the state vector did not improve the results of fitted Q-iteration. The results of the lab experiment, spanning 40 days, indicate that compared to a thermostat controller, the presented approach was able to reduce the total cost of energy consumption of the electric water heater by 15%. Submitted to IEEE transaction on smart grid

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works In this paper, we propose a distributed model predictive control (MPC) scheme for economic dispatch of energy systems with a large number of active components. The scheme uses a distributed optimization algorithm that works over random communication networks and asynchronous updates, implying the resiliency of the proposed scheme with respect to communication problems, such as link failures, data packet drops, and delays. The distributed optimization algorithm is based on the augmented Lagrangian approach, where the dual of the considered convex economic dispatch problem is solved. Furthermore, in order to improve the convergence speed of the algorithm, we adapt Nesterov’s accelerated gradient method and apply the warm start method to initialize the variables. We show through numerical simulations of a well-known case study the performance of the proposed scheme. This work was supported by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie under Grant agreement 675318 (INCITE). Paper no. TSTE-01086-2020 Peer Reviewed